سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,573
تعداد مقالات71,036
تعداد مشاهده مقاله125,507,179
تعداد دریافت فایل اصل مقاله98,771,016
شانظری, حامد, شهرام نیا, امیر مسعود, مسعود نیا, حسین, هرسیج, حسین. (1403). بررسی استفاده از هوش‌مصنوعی در پویایی سیاستگذاری عمومی. , 10(4), 37-53. doi: 10.22059/jppolicy.2024.99825
حامد شانظری; امیر مسعود شهرام نیا; حسین مسعود نیا; حسین هرسیج. "بررسی استفاده از هوش‌مصنوعی در پویایی سیاستگذاری عمومی". , 10, 4, 1403, 37-53. doi: 10.22059/jppolicy.2024.99825
شانظری, حامد, شهرام نیا, امیر مسعود, مسعود نیا, حسین, هرسیج, حسین. (1403). 'بررسی استفاده از هوش‌مصنوعی در پویایی سیاستگذاری عمومی', , 10(4), pp. 37-53. doi: 10.22059/jppolicy.2024.99825
شانظری, حامد, شهرام نیا, امیر مسعود, مسعود نیا, حسین, هرسیج, حسین. بررسی استفاده از هوش‌مصنوعی در پویایی سیاستگذاری عمومی. , 1403; 10(4): 37-53. doi: 10.22059/jppolicy.2024.99825

بررسی استفاده از هوش‌مصنوعی در پویایی سیاستگذاری عمومی

سیاستگذاری عمومی
دوره 10، شماره 4، دی 1403، صفحه 37-53    اصل مقاله (1019.99 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/jppolicy.2024.99825
نویسندگان
حامد شانظری1؛ امیر مسعود شهرام نیا* 2؛ حسین مسعود نیا2؛ حسین هرسیج3
1دانشجوی دکتری سیاستگذاری عمومی، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران
2دانشیار علوم سیاسی، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران
3استاد علوم سیاسی، دانشکده علوم اداری و اقتصاد، دانشگاه اصفهان، اصفهان، ایران
چکیده
چشم‌انداز سیاستگذاری در حال تغییر است؛ زیرا فناوری‌های هوش‌مصنوعی به‌طور فزاینده‌ای در فرایندهای سیاستگذاری ادغام می‌شوند. این مقاله در ابتدا پویایی سیاستگذاری توسط هوش‌مصنوعی را تجزیه‌وتحلیل نموده است و در ادامه بر روی چالش‌ها و فرصت‌های ارائه‌شده توسط این تغییرات، باهدف ارائه بینشی در مورد اینکه چگونه هوش‌مصنوعی می‌تواند کارایی، دقت و پاسخگویی ارزیابی‌های سیاست عمومی را افزایش دهد، بررسی می‌کند. پرسش اصلی مقاله این است که سیاست‌گذاران هنگام ادغام فناوری‌های هوش‌مصنوعی در چرخه سیاستگذاری عمومی با چه چالش‌هایی روبرو هستند؟ فرضیه ما بر این است که ادغام هوش‌مصنوعی در سیاستگذاری عمومی منجر به سیاستگذاری و ارزیابی سیاست دقیق‌تر، حلقه‌های بازخورد سریع‌تر و در نهایت نتایج مؤثرتر سیاست در مقایسه با روش‌های ارزیابی سنتی می‌شود. نتیجه‌گیری نهایی نشان می‌دهد، پردازش داده‌ها از طریق الگوریتم پیشنهادی به همراه تکنیک‌های هوش‌مصنوعی اطلاعات ارزشمندی را برای تصمیم‌گیرندگان عرصه سیاستگذاری عمومی ایجاد می‌کند.
کلیدواژه‌ها
ارزیابی سیاست؛ سیاستگذاری سنتی؛ پویایی سیاستگذاری عمومی؛ هوش‌مصنوعی
عنوان مقاله [English]
Analysis of Using Artificial Intelligence in Dynamic Public Policy
نویسندگان [English]
hamed shanazari1؛ Amirmasoud Shahramnia2؛ Hossein Masoudnia2؛ Hossein Harsij3
1PhD Student of Public Policy, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
2Associate Professor of Political Science, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
3Professor of Political Science, Faculty of Administrative Sciences and Economics, University of Isfahan, Isfahan, Iran
چکیده [English]
The policy landscape is changing; Because artificial intelligence (AI) technologies are increasingly integrated into policy-making processes. This article first analyzes the modernization of policymaking by artificial intelligence and then examines the challenges and opportunities presented by these changes, with the aim of providing insight on how artificial intelligence can increase the efficiency, accuracy and responsiveness of policy evaluations. The main question of the article is what challenges do policy makers face when integrating artificial intelligence technologies into the public policy cycle. Our hypothesis is that the integration of artificial intelligence in public policymaking will lead to more accurate policymaking and policy evaluation, faster feedback loops, and ultimately more effective policy outcomes compared to traditional evaluation methods. The final conclusion shows that our hypothesis is strengthened and data processing through the proposed algorithm along with artificial intelligence techniques creates valuable information for decision makers in the field of public policy. The current research is of a descriptive-analytical type and the researchers have summarized this article using the library method.
کلیدواژه‌ها [English]
Policy Evaluation, Traditional Policy, Modern Policy, Artificial Intelligence
مراجع
  1. Agarwal, P. K. (2018). Public administration challenges in the world of AI and bots. Public Administration Review, 78(6), 917–921.
  2. Akbari, Iman, Danai Fard, Hassan, Abdulhamid, Mahdi, Mahmoudi, Mustafa & Khosravi, Mehdi (2024). Identifying and prioritizing obstacles and challenges of data-driven governance from the perspective of applying artificial intelligence and data-based technologies in the public sector [In Persian]. Strategic studies of public policy, 14(51 special issue), 56-81
  3. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AIguided chatbots. Government Information Quarterly, 36(2), 358–367.
  4. Babaian, Fatemeh, Safdari Ranjbar, Mostafa & Hakim, Amin (2023). Analyzing the role of artificial intelligence in the public policy cycle; A hybrid approach [In Persian]. Journal of Improvement Management Vol. 17 No. 2, Summer 2023 (Serial 60) pp:115-150.
  5. Baraniuk, (2015). NewScientist. Retrieved from https://www.newscientist.com/article/ mg22830412-800-earthquake-artificial-intelligence-knows-where-damage-is-worst/.
  6. Barth, T. J., & Arnold, F. (1999). Artificial intelligence and administrative discretion: Implications for public administration. American Review of Public Administration, 29(4), 332–351.
  7. Birckland, T. (2016). An introduction to the policy process: Theories, concepts, and models of public policy making. New York, NY: Routledge4.
  8. Bredereck, R., Chen, J., Faliszewski, P., Guo, J., Niedermeier, R., & Woeginger, G. J. (2014). Parameterized algorithmics for computational social choice: Nine research challenges. Tsinghua Science and Technology, 19(4), 358–373.
  9. Bridgman, P., & Davis, G. (2003). What use is a policy cycle? Plenty, if the aim is clear. Australian Journal of Public Administration, 62(3), 98–102.
  10. Castro, D., & New, J. (2016). The promise of artificial intelligence. Center for Data Innovation.
  11. Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the “good society”: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528.
  12. Charles, V., Rana, N. P., & Carter, L. (2022). Artificial Intelligence for data-driven decision-making and governance in public affairs. Government Information Quarterly, 39(4), 101742, 41-62.‏
  13. Coglianese, C., & Lehr, D. (2016). Regulating by robot: Administrative decision making in the machine-learning era. The Georgetown Law Journal, 105, 1147.
  14. Corvalán, J. G. (2018). Digital and intelligent public administration: Transformations in the era of artificial intelligence. A&C-Revista de Direito Administrativo & Constitucional, 18(71), 55–87.
  15. Craglia M, Annoni A., Benczur P., Bertoldi P., Delipetrev P., De Prato G., Feijoo C., Fernandez Macias E., Gomez E., Iglesias M., Junklewitz H, López Cobo M., Martens B., Nascimento S., Nativi S., Polvora A., Sanchez I., Tolan S., Tuomi I., Vesnic Alujevic L., Artificial intelligence—A European perspective, EUR 29425 EN, Publications O ce, Luxembourg, 2018, ISBN 978–92-79-97217-1.
  16. Criado, J. I., & Villodre, J. (2020). Delivering public services through social Media in European Local Governments. An interpretative framework using semantic algorithms. Local Government Studies.
  17. Criado, J., & Gil-Garcia, J. (2019). Creating public value through smart technologies and strategies. From digital services to artificial intelligence and beyond. International Journal of Public Sector Management, 32(5), 438–450.
  18. Dehghani Firouzabadi, Seyed Jalal & Cheherazad, Saeed. (2023), Artificial Intelligence and International Relations [In Persian], Tehran, Mizan Publishing House.
  19. Desouza, K. (2018). Delivering artificial intelligence in government: Challenges and opportunities. IBM Center for the Business of Government
  20. Dobell, R., & Zussman, D. (2018). Sunshine, scrutiny, and spending review in Canada, Trudeau to Trudeau: From program evaluation and policy to commitment and results. Canadian Journal of Program Evaluation, 32(3).
  21. Dunn, W. N. (2012). Public policy analysis (5th ed.). New York, NY: Routledge.
  22. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... Galanos, V. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information ManagementArticle 101994 In press.
  23. Engin, Z., & Treleaven, P. (2019). Algorithmic government: Automating public services and supporting civil servants in using data science technologies. The Computer Journal, 62(3), 448–460.
  24. Giest, S. (2017). Big data for policymaking: Fad or fasttrack? Policy Sciences, 50(3), 367–382.
25.Good. (2016). Jet Propulsion Laboratory, California Institute of Technology. Retrieved from https://www.jpl.nasa.gov/news/news.php?feature=6590.

  1. Greenemeier. (2014). Scientific American. Retrieved from https://www. scientificamerican.com/article/smart-machines-join-humans-in-tracking-africaebola-outbreak/.
  2. Grothaus. (2019). Fast company. Retrieved from https://www.fastcompany.com/ 90324512/chinas-airport-facial-recognition-kiosks-should-make-us-fear-for-ourprivacy
  3. Hadden, S. G. (1989). The future of expert systems in government. Journal of Policy Analysis and Management, 8(2), 203.
  4. Höchtl, J., Parycek, P., & Schöllhammer, R. (2016). Big data in the policy cycle: Policy decision making in the digital era. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 147–169.
  5. Howlett, M., McConnell, A., & Perl, A. (2017). Moving policy theory forward: Connecting multiple stream and advocacy coalition frameworks to policy cycle models of analysis. Australian Journal of Public Administration, 76(1), 65–79.
  6. Howlett, M., Ramesh, M., & Perl, A. (2009). Studying public policy: Policy cycles and policy subsystems. Oxford, England: Oxford university press.
  7. Hwa, K. (2018). Three big trends for the future of public safety. Gov Insider.
  8. Jackson, P. C. (2019). Introduction to artificial intelligence. Courier Dover.
  9. Jann, W., & Wegrich, K. (2007). Theories of the policy cycle. Handbook of Public Policy Analysis: Theory, Politics, and Methods, 125, 43–62.
  10. Janssen, M., & Helbig, N. (2018). Innovating and changing the policy-cycle: Policy-makers be prepared!. Government Information Quarterly, 35(4), S99–S105.
  11. Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance. Government Information Quarterly, 33(3), 371–377.
  12. Joyner-Roberson, E. (2019). What do drones, AI and proactive policing have in common? Published in SAS. Retrieved from https://www.sas.com/en/_us/insights/articles/riskfraud/drones-ai-proactive-policing.html.
  13. Keynes, J. M. (2010). Economic possibilities for our grandchildren. Essays in persuasion (pp. 321–332). London, England: Palgrave Macmillan.
  14. Lazer, D. (2016). The rise of the social algorithm. Does content curation by Facebook introduce ideological bias? Science, 348(6239), 1090–1091.
  15. Margetts, H. (2017). The data science of politics. Political Studies Review, 15(2), 201–209.
  16. Margetts, H., & Dorobantu, C. (2019). Rethink government with AI. Nature, 568(April), 163–165.
  17. McKelvey, F., & MacDonald, M. (2019). Artificial intelligence policy innovations at the Canadian Federal Government. Canadian Journal of Communication, 44(2), 43–50.
  18. Meijer, A. (2017). Datapolis: A public governance perspective on “smart cities”. Perspectives on Public Management and Governance, 1(3), 195–203.
  19. Mergel, I. (2017). Digital service teams: Challenges and recommendations for government. IBM Center for the Business of Government. Retrieved from http://kops.uni-konstanz.de/ bitstream/handle/123456789/39164/Mergel_0-409608.pdf?sequence=3& isAllowed=y.
  20. Mergel, I., Gong, Y., & Bertot, J. (2018). Agile government: Systematic literature review and future research.
  21. Mikhaylov, S. J., Esteve, M., & Campion, A. (2018). Artificial intelligence for the public sector: Opportunities and challenges of cross-sector collaboration. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2128), Article 20170357.
  22. Newcombe, T. (2019). Quantum computing holds promise for the public sector. Published in government technology. Retrieved from https://www.govtech.com/products/ Quantum-Computing-Holds-Promise-for-the-Public-Sector.html.
  23. Pencheva, I. (2018). Big data and AI—A transformational shift for government: So, what next for research? Public Policy and Administration, 0(0), 1–21.
  24. Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018). Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Into Imaging, 9(5), 745–753.
  25. Pi, Y. (2021). Machine learning in governments: Benefits, challenges and future directions. JeDEM-eJournal of eDemocracy and Open Government, 13(1), 203-219.‏
  26. Pilar, M., Jost, E., Walsh-Bailey, C., Powell, B. J., Mazzucca, S., Eyler, A., ... & Brownson, R. C. (2022). Quantitative measures used in empirical evaluations of mental health policy implementation: A systematic review. Implementation Research and Practice, 3, 26334895221141116. , 1-24.
  27. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia. Pearson Education.
  28. Savaget, P., Chiarini, T., & Evans, S. (2019). Empowering political participation through artificial intelligence. Science and Public Policy, 46(3), 369–380.
  29. Schuelke-Leech, B. A., Jordan, S. R., & Barry, B. (2019). Regulating autonomy: An assessment of policy language for highly automated vehicles. Review of Policy Research, 36(4), 547–579.
  30. Sheikh Shuai, Hamza. (2021). An overview of international policy in the field of artificial intelligence [In Persian]. International Conference on Management and Industry.
  31. Sigfrids, A., Nieminen, M., Leikas, J., & Pikkuaho, P. (2022). How should public administrations foster the ethical development and use of artificial intelligence? A review of proposals for developing governance of AI. Frontiers in Human Dynamics, 4, 858108,1-19.
  32. Soe, R. M., & Drechsler, W. (2018). Agile local governments: Experimentation before implementation. Government Information Quarterly, 35(2), 323–335.
  33. Soumik, R. (2018). Indian railways leapfrogs into the era of AI. Techwire Asia. Retrieved from https://techwireasia.com/2018/12/indian-railways-leapfrogs-into-the-eraof-ai/.
  34. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383.
  35. Thierer, A. D., Castillo, A., & Russell, R. (2017). Artificial intelligence and public policy. George Mason University. Retrieved from https://www.mercatus.org/publications/ technology-and-innovation/artificial-intelligence-and-public-policy.
  36. Toniolo, F. (2023). Artificial Intelligence and Public Administration: Ethical Implications of the Use of Automatic Decision-making Systems, 22-54.
  37. Valle-Cruz, D. (2019). Public value of e-government services through emerging technologies. International Journal of Public Sector Management.
63.Valle-Cruz, D., Alejandro Ruvalcaba-Gomez, E., Sandoval-Almazan, R., & Ignacio Criado, J. (2019). A review of artificial intelligence in government and its potential from a public policy perspective. 20th annual international conference on digital government research (pp. 91–99). ACM.

  1. Valle-Cruz, D., Criado, J. I., Sandoval-Almazán, R., & Ruvalcaba-Gomez, E. A. (2020). Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation. Government Information Quarterly, 37(4), 101509.
  2. Van der Voort, H. G., Klievink, A. J., Arnaboldi, M., & Meijer, A. J. (2019). Rationality and politics of algorithms. Will the promise of big data survive the dynamics of public decision making? Government Information Quarterly, 36(1), 27–38.
  3. Vetrò, A., Santangelo, A., Beretta, E., & De Martin, J. C. (2019). AI: From rational agents to socially responsible agents. Digital Policy, Regulation and Governance. Retrieved from http://www.antoniovetro.it/authorsversion/journals/2019-dprg-ai.pdf.
  4. Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076–1100.
  5. Wirtz, B., Weyerer, J., & Geyer, C. (2018). Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 1–20.
  6. Zuboff, S. (2019). The age of surveillance capitalism. London, England: Profile Books.
  7. Zuiderwijk, A., Chen, Y. C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government information quarterly, 38(3), 101577, 1-19‏.
آمار
تعداد مشاهده مقاله: 173
تعداد دریافت فایل اصل مقاله: 122





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب